Interested in this AI/ML Engineer role at Emporia Energy?
Apply Now →Skills & Technologies
About This Role
Reports to: SVP, Product
Location: Onsite in Littleton, CO
About Emporia Energy
Emporia is a leading technology company revolutionizing home energy by providing affordable, integrated solutions that empower homeowners to optimize energy usage to reduce costs and carbon footprint. Emporia’s Home Energy Management Platform is a powerful software system that connects their Vue Home Energy Monitor, Level 2 EV Charger, Smart Plugs, Home Battery, and third-party thermostats and appliances. By automating energy use through real-time monitoring and scheduling, the platform optimizes consumption, reduces costs, and simplifies home energy management—all within one intuitive app. With over 400,000 U.S. homes running Emporia’s energy management and EV charging hardware, we are now launching a national Third-Party Owned (TPO) home battery Virtual Power Plant — placing industrial-rated home batteries in residential homes to deliver power redundancy, utility cost savings, and grid peak demand services. By reducing energy waste and saving customers money, we’re helping create a healthier planet and a brighter future. For more information, visit emporiaenergy.com.
Mission, Vision and Values
Emporia was founded in 2018 with a clear purpose to revolutionize home energy, generate tangible customer savings, and accelerate the transition to a lower carbon future. We are striving to become the world's leading home energy management platform and build the largest virtual power plant.
- Customers First: We deliver exceptional service with empathy and responsiveness, building lasting trust in every interaction.
- Own the Outcome: We think like owners, taking responsibility for results and holding ourselves and each other accountable.
- Win Together: We succeed together through honesty, humility and humanity.
- Grit Gets it Done: We work hard, stay passionate, and persevere through challenges.
- Better Every Day: We challenge the status quo, take smart risks and aim to improve 1% every day.
Role Overview
Emporia's home energy products are already in hundreds of thousands of homes: our Level 2 EV chargers, the Vue Energy Monitor, and the Smart Plug. They earn revenue today, they sit in competitive categories, and they carry the install base everything else builds on. This role owns all three.
This is not a new-product job. The work is keeping a mature portfolio healthy, competitive, and profitable: field quality, cost, supply continuity, incremental revisions, and the eventual end-of-life call. The Product Lead is the DRI, the single-person decision maker for product definition, development, and launch. They do not necessarily do all the work, but they ensure the work gets done. Where this description says "own," it means accountable for the decision and the outcome, not doing the work alone. On this portfolio that means every change that ships, from a firmware release to a board revision to a price move. Operations and Sustaining run field service, RMA processing, and production; you own the product decisions behind them.
You report to SVP Product. Revisions and new SKUs still move through the same product development lifecycle (PDLC) as any new product, just in shorter cycles. We move quickly by being disciplined, not by skipping process. The same stage-gates and deliverables stay in place: PRD, PCD, business case, design reviews, launch readiness. AI should accelerate the work to be done and minimize and remove hand offs along the way.
Responsibilities
Portfolio & Roadmap: Owning What Ships
Own the roadmap across all three lines and the prioritization between them.
- Own the roadmap for EVSE, Vue, and Smart Plug, and make the call on where effort goes when all three want the same engineering week.
- Own competitive positioning and pricing in crowded categories, tracking what competitors ship and what it means for our roadmap.
- Build the business case for every change you propose, with AI as-needed: cost, margin, volume, and payback.
- Turn support tickets, telemetry, reviews, and RMA data into a ranked list of what to fix and what to build, using AI to read the volume rather than sampling it.
Field Quality & Sustaining: The Core of the Job
Shipping products fail in the field, and the product side owns the answer to why.
- Own the sustaining roadmap: firmware releases, field-quality issues, and warranty and reliability trends feeding back into requirements.
- Drive root cause on field failures with Engineering, Quality, and Customer Support, and decide what gets fixed in firmware, in hardware, or in documentation.
- Partner with Customer Support so the top drivers of contacts and returns get engineered out, not just answered faster.
- Use AI to close knowledge gaps in failure data, logs, and schematics, so your discussions with subject matter experts start further along.
Cost, Supply & Compliance
On mature products, margin and continuity are where the leverage is.
- Drive cost-down: component substitutions, design-for-cost changes, and supplier negotiation, protecting margin as categories get more competitive.
- Stay ahead of component obsolescence and single-source risk, qualifying replacements and second sources before they become urgent.
- Drive compliance continuity with Engineering and Compliance, including recertification when a component or design changes (UL, FCC, Energy Star, and others as they apply).
- Work with Operations on volume planning, yield, and quality so supply keeps up with demand.
Revisions, New SKUs & EOL
Incremental releases are still real product launches, just faster.
- Define and drive hardware revisions, new variants, and SKU additions through the same gates, at a cycle time that fits the size of the change.
- Write the requirements, negotiate scope and timeline with Engineering and Operations to a shared commitment, and make the final call on when and how each change ships.
- Own launch and go-to-market for revisions and new SKUs working with Marketing, BD, and Customer Support, including channel and support readiness.
- Own end-of-availability and end-of-life decisions, with a transition plan for customers and channel.
Driving AI Adoption: Across Every Phase
Native AI-use runs through the whole job. Gates, deliverables, and approvals stay in place. The waiting between them goes. Push that on the team, not just yourself.
- Automate and optimize the manual, slow, or expensive steps you find in the PDLC. Push yourself and the broader team to challenge the default approach without diluting the deliverable.
- Use AI to build at the edge of capability. Exemplify AI-enabled execution, encouraging Engineering, Operations, and partners to push past their traditional approach and deliver faster.
- Build and share your own tools, prompts, and workflows instead of asking for headcount.
- Be honest about where AI is not working, and feed real evidence back into how Emporia adopts it.
Required Qualifications & Skills
- 5+ years in product management for hardware, including real ownership of shipping products and not only new product introduction.
- A track record as a DRI, accountable for outcomes and not just definition, comfortable with ambiguity and parallel workstreams.
- Experience owning sustaining work: field quality, warranty and RMA analysis, firmware release decisions, and root cause with engineering.
- A track record of cost-down and margin improvement on products already in production.
- Experience with component obsolescence, second sourcing, and supply continuity.
- Comfort with competitive and pricing decisions in a crowded consumer category.
- Heavy, fluent use of AI tools (Claude, ChatGPT, Grok or similar), plus a habit of building your own tooling instead of asking for headcount, and getting others to adopt it.
- Business sense: you can own a business case, unit economics, and margin trade-offs leadership will bet on.
- Strong cross-functional leadership: you lead through influence and escalate well without formal reporting authority.
- Experience running a formal gate process for hardware, and a belief that discipline is what makes speed possible.
Desired Qualifications & Skills
- EV charging, energy monitoring, smart plugs, or other connected home hardware.
- Consumer electronics sold at retail and direct-to-consumer scale.
- Recertification experience (UL, FCC, Energy Star) after design or component changes.
- High-volume manufacturing quality work: DPPM, FAI, yield, and RMA analysis.
- Fleet telemetry and cohort analysis on a large install base.
- A career spanning both product management and program management, in a fast-paced, test and validation driven environment.
What We Offer
Compensation: The expected base salary range is $130,000 - $170,000, plus stock options. The offered salary will depend on numerous factors, including but not limited to work experience, skills, and internal equity considerations. The base salary does not include benefits or other incentives.
Benefits: medical/dental/vision insurance, 401(k), employee stock option program, paid holidays plus unlimited PTO, discounts on Emporia products and partner offerings.
Career Growth: Be part of a fast-growing company at the forefront of home energy innovation.
Emporia Energy is an equal opportunity employer. All applicants will be considered for employment without regard to race, color, ancestry, national origin, sex, gender, sexual orientation, marital status, religion, age, disability, gender identity, results of genetic testing, service in the military, or any other characteristic protected by applicable federal, state or local laws. We will make reasonable accommodations for any qualified applicant with a disability, provided that the individual is otherwise qualified to safely perform the essential functions of the job with or without accommodation and that the accommodation would not impose an undue hardship on the operation of our business. Please let us know if you believe you require reasonable accommodation, or if you would like assistance to complete an application or to participate in an interview at the company.
New employees will be required to successfully complete a background check.
Any communication to applicants relating to the Emporia Energy hiring process will only come from email addresses with the domains emporiaenergy.com or [email protected]. If in the course of the application or hiring process with Emporia Energy you are contacted through another domain, are requested to provide banking or other sensitive information, or you note any other suspicious activity, please contact [email protected].
Salary Context
This $130K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Emporia Energy, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($150K) sits 29% below the category median. Disclosed range: $130K to $170K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Emporia Energy AI Hiring
Emporia Energy has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Littleton, CO, US. Compensation range: $170K - $170K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.